12 July 2018

Content

  • Introduction to metabolomics
  • Preprocessing of LC-MS data in Bioconductor

Metabolomics?

  • Large-scale study of small molecules (metabolites) in a system (cell, tissue, organism).
  • Metabolites: intermediates and products of cellular processes.
  • Metabolome?
    • Genome: what can happen.
    • Transcriptome: what appears to be happening.
    • Proteome: what makes it happen.
    • Metabolome: what actually happened.
  • Metabolome influenced by genetic and environmental factors.

Metabolites? Metabolomics?

How can we measure metabolites?

  • Nuclear Magnetic Resonance (NMR) - not covered here.
  • Mass spectrometry (MS)-based metabolomics.
  • Targeted metabolomics TODO describe
  • Untargeted metabolomics TODO describe

Mass Spectrometry (MS)

Mass Spectrometry (MS)

  • Problem: unable to distinguish between metabolites with the same mass-to-charge ratio (m/z).
  • Solution: separate metabolites prior to MS by another property.

Liquid Chromatography Mass Spectrometry (LC-MS)

Liquid Chromatography Mass Spectrometry (LC-MS)

Liquid Chromatography Mass Spectrometry (LC-MS)

Liquid Chromatography Mass Spectrometry (LC-MS)

Liquid Chromatography Mass Spectrometry (LC-MS)

Liquid Chromatography Mass Spectrometry (LC-MS)

Liquid Chromatography Mass Spectrometry (LC-MS)

Liquid Chromatography Mass Spectrometry (LC-MS)

LC separation

  • list some possibilities how metabolites could be separated.
  • Example HILIC.

LC-MS data preprocessing

  • Chromatographic peak detection
  • Alignment
  • Correspondence

Chromatographic peak detection

  • Aim: identify chromatographic peaks in the data.

Chromatographic peak detection

  • Aim: identify chromatographic peaks in the data.

Chromatographic peak detection

  • centWave [Tautenhahn et al. BMC Bioinformatics, 2008]:
  • Step 1: identify regions of interest.

Chromatographic peak detection

  • Step 2: peak detection using continuous wavelet transform.
  • Allows detection of peaks with different widths.

Chromatographic peak detection

with xcms

  • After reading the data with readMSData (MSnbase package):
  • findChromPeaks function, passing settings along with an algorithm-specific parameter object.
## Perform centWave peak detection on the MSnExp object
cwp <- CentWaveParam(peakwidth = c(2, 10), snthresh = 5)
data <- findChromPeaks(data, param = cwp)

Alignment

  • Chromatography subject to (random and systematic) noise.
  • Same analyte may elute at different time.

TODO add content: - image describing alignment. - available methods. - what is the result? - Caveats: - assumes analytes elute in the same order. - heavily LC setup-dependent.

Correspondence

  • Aim: group peaks across samples, assuming that they represent the same ion.

TODO add content: - image describing the alignment - available methods - what is the result? (m/z - rt ranges).

Normalization

TODO add content: - what sources of variation? - Crucial to add QC controls in the experiment. - available methods (RUV, linear models). - MS runs not very expensive, running replicates, QC controls etc usually not a problem.

Identification

  • Match compounds based on features’ m/z.
  • Lab-internal databases with approximate retention times for specific compounds.

TODO add content: - What databases available - up and coming: compound db, similar to ensembldb and alike. Problem: unclear license situation.

Afternoon lab

  • LC-MS data handling (MSnbase).
  • LC-MS data preprocessing using xcms.